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SOME OBSERVATIONS ON THE DESIGN OF EARLY STAGE CLINICAL TRIALS IN THE PHARMACEUTICAL INDUSTRY Hans Hockey Biometrics Matters Limited (BML) 13 Nevada Road.

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Presentation on theme: "SOME OBSERVATIONS ON THE DESIGN OF EARLY STAGE CLINICAL TRIALS IN THE PHARMACEUTICAL INDUSTRY Hans Hockey Biometrics Matters Limited (BML) 13 Nevada Road."— Presentation transcript:

1 SOME OBSERVATIONS ON THE DESIGN OF EARLY STAGE CLINICAL TRIALS IN THE PHARMACEUTICAL INDUSTRY Hans Hockey Biometrics Matters Limited (BML) 13 Nevada Road Hamilton 3216 New Zealand hans@biometricsmatters.com IBC, Kobe, Japan, August 2012 EMR-IBS, Tel Aviv, 23 April 2013 1

2 Structure of talk 2

3 There is no structure! 3

4 Five Case Studies 1)Two doses plus placebo 2)“Factorial” dose escalation and food effect 3)3-treatment, 3-period cross-over design 4)Escalating dose study with placebo substitution plus 5)Augmented placebo insertion and food effect 4

5 5 Take-home points 1.Use contrasts that are orthogonal, and are models, not tests 2.Design programs to include more combined studies, including factorial and similar designs 3.Don’t worry about ‘imbalance’ 4.Practical matters can matter more than statistical issues 5.In cross-over designs, never ever fit Sequence 6.Use mixed, not fixed, models for maximum information extraction

6 6 Take-home point 1 1.Use contrasts that are orthogonal, and are models, not tests. 2.Design programs to include more combined studies, including factorial and similar designs 3.Don’t worry about ‘imbalance’ 4.Practical matters can matter more than statistical issues 5.In cross-over designs, never ever fit Sequence 6.Use mixed, not fixed, models for maximum information extraction

7 7 Treatment 5 mg10 mgPlacebo Case Study 1

8 8 Treatment Contrast5 mg10 mgPlacebo 10 v placebo01 5 v placebo10 Case Study 1

9 9 Treatment Contrast5 mg10 mgPlacebo 10 v placebo01 5 v placebo10 Dose (mg) Contrast0510 Linear01 Quadratic2 Case Study 1

10 10 Treatment Contrast5 mg10 mgPlacebo 10 v placebo01 5 v placebo10 112 Dose (mg) Contrast0510 Linear01 Quadratic2 222 Case Study 1

11 11 Treatment Contrast5 mg10 mgPlacebo 10 v placebo01 5 v placebo10 Dose (mg) Contrast0510 Linear01 Quadratic2 A modeller, not a tester, would prefer even more dose levels Case Study 1

12 12

13 13

14 14 Take-home point 2 1.Use contrasts that are orthogonal, and are models, not tests 2.Design programs to include more combined studies, including factorial and similar designs 3.Don’t worry about ‘imbalance’ 4.Practical matters can matter more than statistical issues 5.In cross-over designs, never ever fit Sequence 6.Use mixed, not fixed, models for maximum information extraction

15 A typical Phase 1 drug development program list of studies Over 50 studies, many are escalating doses or 2x2 crossover designs 15

16 16

17 17

18 Combining studies Ronald Fisher argued in 1926 that "complex" designs (such as factorial designs) were more efficient than studying one factor at a time. Fisher wrote, "No aphorism is more frequently repeated in connection with field trials, than that we must ask Nature few questions, or, ideally, one question, at a time. The writer is convinced that this view is wholly mistaken." Nature, he suggests, will best respond to "a logical and carefully thought out questionnaire". A factorial design allows the effect of several factors and even interactions between them to be determined with the same number of trials as are necessary to determine any one of the effects by itself with the same degree of accuracy. (Wikipedia) 18

19 19

20 Case Study 2 20 Combining studies (part 1)

21 Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses A Phase 1 PK study Design 1 CohortsSubjects Period 1234 1 1-4150 fasted150 fed 5-8150 fed150 fasted 2 9-12300 fasted300 fed 13-16300 fed300 fasted 3 17-20600 fasted600 fed 21-24600 fed600 fasted Fed v fasted always a 2x2 crossover Too long! (7-day washout three times) 21

22 Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses A Phase 1 PK study Design 2 CohortsSubjects Period 1234 1 1-4150 fasted150 fed 5-8150 fed150 fasted 2 9-12300 fasted300 fed 13-16300 fed300 fasted 3 17-20600 fasted600 fed 21-24600 fed600 fasted 22

23 Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses A Phase 1 PK study Design 2 CohortsSubjects Period 1234 1 1-4 150 fasted 5-8 2 9-12300 fasted300 fed 13-16300 fed300 fasted 3 17-20 600 fasted 21-24 23

24 Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses A Phase 1 PK study Design 3 CohortsSubjects Period 1234 11-12150 fasted 2 13-18 300 fasted300 fed 19-24 300 fed300 fasted 11-12600 fasted 24

25 A Phase 1 PK study – Design 3 4, not 6, treatments (6 not 12 cells) Only one washout period Safe escalation What assumptions? Two analyses? NO! 25

26 Should one 3x3 cross-over study with N subjects replace two 2x2 cross-over studies with 2N subjects total? That is, why not compare A & B & C together instead of A & B separately from A & C? Are three periods too long? Worry that both the A-B and A-C comparisons depend on A treatment being well estimated A typical example:A - market formulation (fasted) B - research formulation (fasted) C - market formulation (fed) Combining studies (part 2) 26

27 27

28 Case Study 3 Capsule v tablet & Fed v fast for capsules 28

29 29 Bioequivalence Food effect AB C

30 30

31 31 Formulation effect (1 v 2) Food effect (A v B) A1A2 B2B1

32 NOT a Case Study Not seen by me anyhow 32

33 33 Take-home point 3 1.Use contrasts that are orthogonal, and are models, not tests 2.Design programs to include more combined studies, including factorial and similar designs 3.Don’t worry about ‘imbalance’ 4.Practical matters can matter more than statistical issues 5.In cross-over designs, never ever fit Sequence 6.Use mixed, not fixed, models for maximum information extraction

34 34 Take-home point 3 1.Use contrasts that are orthogonal, and are models, not tests 2.Design programs to include more combined studies, including factorial and similar designs 3.Not every PK crossover subject needs a placebo 4.Practical matters can matter more than statistical issues 5.In cross-over designs, never ever fit Sequence 6.Use mixed, not fixed, models for maximum information extraction

35 Design: This is a single-blind, placebo-controlled, randomised, “cross-over”, single-dose escalation study in which the toleration, safety and pharmacokinetics of XYZ-123,456 will be investigated. Both ‘cross-over’ and escalation? Two groups or cohorts, 8 each 35 Case Study 4

36 SubjectsWeek 1Week 3Week 5Week 7 1&2Placebo1520 3&41Placebo520 5&615Placebo20 7&81520Placebo Group A doses (mg) over time 8 subjects per dose, 8 per Placebo (Same pattern for Group B doses, but 2.5, 10 & 40 mg) 36

37 SubjectsWeek 1Week 3Week 5Week 7 1&2Placebo1520 3&41Placebo520 5&615Placebo20 7&81520Placebo Placebo insertion design 8 subjects per dose, 8 per Placebo (Same pattern for Group B doses, but 2.5, 10 & 40 mg) Placebo insertion 37

38 SubjectsWeek 1Week 3Week 5 1&2Placebo520 3&41Placebo20 5&615Placebo Placebo substitution 4 subjects per dose, all 6 per Placebo (Placebo is of least interest for PK, but needed for safety comparisons) 38

39 SubjectsWeek 1Week 3Week 5 1&2Placebo520 3&41Placebo20 5&615Placebo 7&81520 Why not Placebo substitution plus! 6 subjects per dose, all 6 per Placebo (Placebo is of least interest for PK, but needed for safety comparisons) 6 per treatment! 39

40 SubjectsWeek 1Week 3Week 5 1&2Placebo520 3&41Placebo20 5&615Placebo 7&81520 “¾ placebo substitution” Embrace imbalance! 40

41 SubjectsWeek 1Week 3Week 5ABCD drop BIBD 1&20520BACD 3&41020CBAD 5&6150DBCA 7&81520ABCD ¾ placebo substitution Embrace imbalance! (but it isn’t unbalanced! – it’s a BIBD) 41

42 42 Take-home point 4 1.Use contrasts that are orthogonal, and are models, not tests 2.Design programs to include more combined studies, including factorial and similar designs 3.Don’t worry about ‘imbalance’ 4.Practical matters can matter more than statistical issues 5.In cross-over designs, never ever fit Sequence 6.Use mixed, not fixed, models for maximum information extraction

43 Case Study 5 Still don’t worry about imbalance (it also is a BIBD, but plus extra replications of non-placebo sequences) Do worry about practical issues (in this case, not sure if 10, 11 or 12 pre-screened subjects will turn up on Day 1, and less on Day 2) 43

44 SubjectDayDose per day orFirstSecondFirstSecond SequenceAMPMAMPM 1E0E2A4A8212 2A0A2E4E8212 3E2E0A4A8212 4A2A0E4E8212 5E2E4A0A868 6A2A4E0E868 7E2E4A8A068 8A2A4E8E068 9E2E4A8 616 10A2A4E8 616 11E2E4A8 616 12A2A4E8 616 Augmented insertion design for 12 subjects over 4 sessions in 2 days Ex = Food (fEd) with x capsules Ax = Fasted (fAst) with x capsules (from final protocol) 44

45 Practical Design Issues The design includes placebo insertion such that placebo occurs twice in each of 4 sessions, with double blinding. Each subject has exposure to each active dose, with subjects 9-12 receiving the maximum of two 8-capsule sessions in the one day, after having been exposed to 4 capsules the previous test day. Total exposure ranges from 2 to 16 capsules per day per subject. The design is robust to not having all planned 12 subjects available as there is double replication of the sequences 9 and 10 (sequences 11 and 12). Random allocation of sequences to subjects will be arranged such that if there is a shortfall of 1 or 2 subjects then sequence 12 and then 11 will not be allocated. If there is a further shortfall (very unlikely) then all missing Day 1 subjects will be replaced. All sequences/subjects include the highest dose on Day 2, so given that 10 to 12 subjects completed Day 1, there is no imperative to replace subjects if up to 2 do not attend Day 2. If the Day 2 discontinuation rate is higher though, then consideration will again be given to subject replacement. 45

46 46 Take-home point 5 1.Use contrasts that are orthogonal, and are models, not tests 2.Design programs to include more combined studies, including factorial and similar designs 3.Don’t worry about ‘imbalance’ 4.Practical matters can matter more than statistical issues 5.In cross-over designs, never ever fit Sequence 6.Use mixed, not fixed, models for maximum information extraction

47 Advantages of cross-overs Using within subject variation gives increased precision and power Lower costs (usually extra subjects more expensive than extra periods) 47

48 Advantages of cross-overs Using within subject variation gives increased precision and power Lower costs Sequence, carryover and treatment.period are all aliased Disadvantage of 2x2 cross-overs 48

49 2x2 cross-over ANOVA table (12 subjects) d.f. Subjects Sequence1 Between subject error10 Total11 Periods within subjects Treatment1 Period1 Within subject error10 Total12 TOTAL23 49

50 2x2 cross-over ANOVA table (12 subjects) d.f. Subjects Carryover1 Between subject error10 Total11 Periods within subjects Treatment1 Period1 Within subject error10 Total12 TOTAL23 50

51 2x2 cross-over ANOVA table (12 subjects) d.f. Subjects Treatment.Period1 Between subject error10 Total11 Periods within subjects Treatment1 Period1 Within subject error10 Total12 TOTAL23 51

52 2x2 cross-over ANOVA table (12 subjects) d.f. Subjects Sequence1 Between subject error9 Total10 Periods within subjects Treatment1 Period1 Within subject error10 Total12 TOTAL23 52

53 2x2 cross-over ANOVA table (12 subjects) d.f. Subjects Sequence1 Between subject error9 Total10 Periods within subjects Treatment1 Sequence.Treatment1 Within subject error10 Total12 TOTAL23 53

54 My personal preference! d.f. Subjects Treatment.Period1 Between subject error10 Total11 Periods within subjects Treatment1 Period1 Within subject error10 Total12 TOTAL23 54

55 55 Take-home point 6 1.Use contrasts that are orthogonal, and are models, not tests 2.Design programs to include more combined studies, including factorial and similar designs 3.Don’t worry about ‘imbalance’ 4.Practical matters can matter more than statistical issues 5.In cross-over designs, never ever fit Sequence 6.Use mixed, not fixed, models for maximum information extraction

56 References Any of the classic design texts of the 1950s, such as: Cochran and Cox (1950) Kempthorne (1952) Cox (1958) Quenouille (1958) and even Fisher (1935) and Yates (1937)! 56

57 References (cont’d) 57

58 Thank you for your attention! This presentation has been brought to you by: 58

59 Thank you for your attention! This presentation has been brought to you by: 59


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